In this project, we explore the development of a machine learning model to predict housing prices. Accurate price prediction can help investors, policymakers, and urban planners make informed decisions.
Housing price prediction is challenging due to the multifaceted nature of real estate markets and varying economic factors.
With the availability of large datasets and advanced machine learning techniques, we aim to build a robust model that balances complexity with interpretability.
Data preparation is the foundation of a successful machine learning project. In this section, we load, clean, and preprocess our dataset.
We use pandas to read in our CSV file containing housing data.
import pandas as pd
# Load the dataset
data = pd.read_csv('housing_prices.csv')
print(data.head())
Next, we handle missing values and encode categorical variables.
# Fill missing values using forward fill
data.fillna(method='ffill', inplace=True)
# Convert categorical variables using one-hot encoding
data = pd.get_dummies(data, drop_first=True)
In this section, we build a machine learning model using scikit-learn, split the data, and train a Random Forest Regressor.
We use a Random Forest Regressor to capture complex interactions in the data.
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
# Separate features and target variable
X = data.drop('price', axis=1)
y = data['price']
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Initialize and train the Random Forest model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
We carefully tune the model parameters to avoid overfitting and ensure that the model generalizes well to unseen data.
After training the model, we assess its performance using standard evaluation metrics.
We use Mean Absolute Error (MAE) and R-squared to measure the model's accuracy.
from sklearn.metrics import mean_absolute_error, r2_score
# Make predictions on the test set
y_pred = model.predict(X_test)
# Calculate evaluation metrics
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print("Mean Absolute Error:", mae)
print("R-squared:", r2)
A lower MAE and a higher R-squared indicate a better model performance. However, these metrics must be interpreted in the context of the data and the problem domain.
Despite promising results, there are important cautions to consider: